from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2021-12-08 14:04:32.009479
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 08, Dec, 2021
Time: 14:04:36
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -47.4140
Nobs: 499.000 HQIC: -47.8756
Log likelihood: 5736.90 FPE: 1.19788e-21
AIC: -48.1738 Det(Omega_mle): 1.00198e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.380258 0.081301 4.677 0.000
L1.Burgenland 0.094328 0.044270 2.131 0.033
L1.Kärnten -0.116052 0.022710 -5.110 0.000
L1.Niederösterreich 0.167416 0.091752 1.825 0.068
L1.Oberösterreich 0.129421 0.092961 1.392 0.164
L1.Salzburg 0.282105 0.047471 5.943 0.000
L1.Steiermark 0.015939 0.061298 0.260 0.795
L1.Tirol 0.107293 0.049512 2.167 0.030
L1.Vorarlberg -0.085487 0.043602 -1.961 0.050
L1.Wien 0.031809 0.083352 0.382 0.703
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.017641 0.180457 0.098 0.922
L1.Burgenland -0.051704 0.098261 -0.526 0.599
L1.Kärnten 0.036701 0.050408 0.728 0.467
L1.Niederösterreich -0.221350 0.203653 -1.087 0.277
L1.Oberösterreich 0.481968 0.206338 2.336 0.020
L1.Salzburg 0.312908 0.105366 2.970 0.003
L1.Steiermark 0.099097 0.136058 0.728 0.466
L1.Tirol 0.308059 0.109898 2.803 0.005
L1.Vorarlberg 0.008003 0.096780 0.083 0.934
L1.Wien 0.019276 0.185009 0.104 0.917
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.226445 0.041398 5.470 0.000
L1.Burgenland 0.090364 0.022542 4.009 0.000
L1.Kärnten -0.004720 0.011564 -0.408 0.683
L1.Niederösterreich 0.220821 0.046719 4.727 0.000
L1.Oberösterreich 0.168827 0.047335 3.567 0.000
L1.Salzburg 0.035847 0.024172 1.483 0.138
L1.Steiermark 0.025719 0.031213 0.824 0.410
L1.Tirol 0.075470 0.025211 2.993 0.003
L1.Vorarlberg 0.055799 0.022202 2.513 0.012
L1.Wien 0.107006 0.042442 2.521 0.012
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.160184 0.040379 3.967 0.000
L1.Burgenland 0.042906 0.021987 1.951 0.051
L1.Kärnten -0.012415 0.011279 -1.101 0.271
L1.Niederösterreich 0.148537 0.045569 3.260 0.001
L1.Oberösterreich 0.350329 0.046170 7.588 0.000
L1.Salzburg 0.100110 0.023576 4.246 0.000
L1.Steiermark 0.107619 0.030444 3.535 0.000
L1.Tirol 0.086206 0.024590 3.506 0.000
L1.Vorarlberg 0.053378 0.021655 2.465 0.014
L1.Wien -0.037385 0.041397 -0.903 0.366
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.159894 0.077773 2.056 0.040
L1.Burgenland -0.041619 0.042349 -0.983 0.326
L1.Kärnten -0.036405 0.021725 -1.676 0.094
L1.Niederösterreich 0.126533 0.087770 1.442 0.149
L1.Oberösterreich 0.192498 0.088928 2.165 0.030
L1.Salzburg 0.255002 0.045411 5.615 0.000
L1.Steiermark 0.073024 0.058638 1.245 0.213
L1.Tirol 0.130402 0.047364 2.753 0.006
L1.Vorarlberg 0.104425 0.041710 2.504 0.012
L1.Wien 0.039542 0.079735 0.496 0.620
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.081931 0.061616 1.330 0.184
L1.Burgenland 0.014518 0.033551 0.433 0.665
L1.Kärnten 0.051472 0.017211 2.991 0.003
L1.Niederösterreich 0.177122 0.069536 2.547 0.011
L1.Oberösterreich 0.340215 0.070453 4.829 0.000
L1.Salzburg 0.049947 0.035977 1.388 0.165
L1.Steiermark -0.006856 0.046456 -0.148 0.883
L1.Tirol 0.123053 0.037524 3.279 0.001
L1.Vorarlberg 0.058610 0.033045 1.774 0.076
L1.Wien 0.111748 0.063170 1.769 0.077
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.168823 0.074805 2.257 0.024
L1.Burgenland 0.010920 0.040732 0.268 0.789
L1.Kärnten -0.060891 0.020896 -2.914 0.004
L1.Niederösterreich -0.112344 0.084420 -1.331 0.183
L1.Oberösterreich 0.235382 0.085534 2.752 0.006
L1.Salzburg 0.038207 0.043678 0.875 0.382
L1.Steiermark 0.264067 0.056401 4.682 0.000
L1.Tirol 0.489302 0.045556 10.741 0.000
L1.Vorarlberg 0.070616 0.040118 1.760 0.078
L1.Wien -0.101244 0.076692 -1.320 0.187
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.139419 0.082642 1.687 0.092
L1.Burgenland -0.013454 0.045000 -0.299 0.765
L1.Kärnten 0.064111 0.023085 2.777 0.005
L1.Niederösterreich 0.171235 0.093265 1.836 0.066
L1.Oberösterreich -0.073385 0.094495 -0.777 0.437
L1.Salzburg 0.221972 0.048254 4.600 0.000
L1.Steiermark 0.134059 0.062310 2.151 0.031
L1.Tirol 0.049907 0.050329 0.992 0.321
L1.Vorarlberg 0.141850 0.044322 3.200 0.001
L1.Wien 0.168088 0.084727 1.984 0.047
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.456053 0.045655 9.989 0.000
L1.Burgenland -0.000984 0.024860 -0.040 0.968
L1.Kärnten -0.013289 0.012753 -1.042 0.297
L1.Niederösterreich 0.176519 0.051524 3.426 0.001
L1.Oberösterreich 0.268937 0.052203 5.152 0.000
L1.Salzburg 0.018963 0.026657 0.711 0.477
L1.Steiermark -0.013889 0.034423 -0.403 0.687
L1.Tirol 0.069442 0.027804 2.498 0.013
L1.Vorarlberg 0.055985 0.024485 2.286 0.022
L1.Wien -0.016011 0.046807 -0.342 0.732
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.026353 0.091168 0.154595 0.138505 0.063799 0.082184 0.015496 0.207475
Kärnten 0.026353 1.000000 -0.036870 0.127666 0.047982 0.072899 0.456436 -0.082170 0.094085
Niederösterreich 0.091168 -0.036870 1.000000 0.278195 0.098238 0.252483 0.051779 0.141074 0.244491
Oberösterreich 0.154595 0.127666 0.278195 1.000000 0.191825 0.285330 0.162827 0.124132 0.178896
Salzburg 0.138505 0.047982 0.098238 0.191825 1.000000 0.119523 0.061588 0.109547 0.063567
Steiermark 0.063799 0.072899 0.252483 0.285330 0.119523 1.000000 0.132672 0.087302 0.004621
Tirol 0.082184 0.456436 0.051779 0.162827 0.061588 0.132672 1.000000 0.063814 0.128733
Vorarlberg 0.015496 -0.082170 0.141074 0.124132 0.109547 0.087302 0.063814 1.000000 -0.011829
Wien 0.207475 0.094085 0.244491 0.178896 0.063567 0.004621 0.128733 -0.011829 1.000000